from pyspark.sql import SparkSessionimport pandas as pdimport plotly.express as pximport plotly.io as piopio.renderers.default ="svg"import reimport numpy as npimport plotly.graph_objects as gofrom pyspark.sql.functions import col, split, explode, regexp_replace, transform, whenfrom pyspark.sql import functions as Ffrom pyspark.sql.functions import col, monotonically_increasing_idnp.random.seed(42)pio.renderers.default ="notebook"# Initialize Spark Sessionspark = SparkSession.builder.appName("./data/LightcastData").getOrCreate()# Load Datadf = spark.read.option("header", "true").option("inferSchema", "true").option("multiLine","true").option("escape", "\"").csv("./data/lightcast_job_postings.csv")# Show Schema and Sample Data#print("---This is Diagnostic check, No need to print it in the final doc---")#df.printSchema() # comment this line when rendering the submission#df.show(5)
WARNING: Using incubator modules: jdk.incubator.vector
Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
25/09/22 04:31:22 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
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